library(tibble)
library(readxl)
library(dplyr)
library(gtExtras)
library(ggplot2)
library(plotly)
library(viridis)
library(hrbrthemes)
setwd("C:/Users/felip/Desktop/R/3_analise")
data <- readxl::read_xlsx ("BPAs_FDTs_ RAVLTs_EscoreZ.xlsx", sheet = 1)
data
#gt_plt_summary(data)
####################### BPA ANALYSIS
summary(data)
ID GROUP EDU_LEVEL AGE
Length:117 Length:117 Length:117 Min. :20.00
Class :character Class :character Class :character 1st Qu.:26.00
Mode :character Mode :character Mode :character Median :31.00
Mean :32.91
3rd Qu.:39.00
Max. :49.00
LIMIT_AGE BPA_CONC_POINTS BPA_CONC_EscoreZ BPA_DIVID_POINTS
Length:117 Min. : 44.00 Min. :-2.0037 Min. : 26.00
Class :character 1st Qu.: 80.00 1st Qu.:-0.2828 1st Qu.: 72.00
Mode :character Median : 92.00 Median : 0.3186 Median : 80.00
Mean : 92.15 Mean : 0.3112 Mean : 80.88
3rd Qu.:105.00 3rd Qu.: 0.8875 3rd Qu.: 94.00
Max. :120.00 Max. : 1.7190 Max. :116.00
BPA_DIVID_EscoreZ BPA_ALTERN_POINTS BPA_ALTERN_EscoreZ
Min. :-1.8505 Min. : 50.00 Min. :-1.5930
1st Qu.:-0.1214 1st Qu.: 87.00 1st Qu.: 0.1048
Median : 0.2789 Median : 99.00 Median : 0.6705
Mean : 0.3513 Mean : 97.28 Mean : 0.5853
3rd Qu.: 0.8575 3rd Qu.:110.00 3rd Qu.: 1.1660
Max. : 1.8387 Max. :120.00 Max. : 1.8333
#utils::View(data)
p1 <- data %>% select(ID,EDU_LEVEL ,GROUP ,EDU_LEVEL , AGE, BPA_CONC_POINTS , BPA_CONC_EscoreZ )
p2 <- data %>% select(ID,EDU_LEVEL ,GROUP ,EDU_LEVEL, AGE, BPA_DIVID_POINTS , BPA_DIVID_EscoreZ )
p3 <- data %>% select(ID,EDU_LEVEL ,GROUP ,EDU_LEVEL ,AGE, BPA_ALTERN_POINTS , BPA_ALTERN_EscoreZ )
#################################### P1
coresBPA <- c("#1a2887", "#799de4")
p1 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", BPA_CONC_POINTS,
"\nZ-Score: ", BPA_CONC_EscoreZ, sep="")) %>%
ggplot(aes(x = AGE, y = BPA_CONC_EscoreZ, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresBPA) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score BPA Focused Attention", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p1
pp1 <- ggplotly(p1, tooltip="text")
pp1
################################## P2
p2 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", BPA_DIVID_POINTS,
"\nZ-Score: ", BPA_DIVID_EscoreZ, sep="")) %>%
ggplot(aes(x = AGE, y = BPA_DIVID_EscoreZ, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresBPA) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score BPA Focused Attention", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p2
pp2 <- ggplotly(p2, tooltip="text")
pp2
###################################### P3
p3 <- data %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", BPA_ALTERN_POINTS,
"\nZ-Score: ", BPA_ALTERN_EscoreZ, sep="")) %>%
ggplot(aes(x = AGE, y = BPA_ALTERN_EscoreZ, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresBPA) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score BPA Focused Attention", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p3
pp3 <- ggplotly(p3, tooltip="text")
pp3
NA
NA
NA
NA
library(tibble)
library(readxl)
library(dplyr)
library(gtExtras)
library(ggplot2)
library(plotly)
library(viridis)
library(hrbrthemes)
setwd("C:/Users/felip/Desktop/R/3_analise")
data <- readxl::read_xlsx ("BPAs_FDTs_ RAVLTs_EscoreZ.xlsx", sheet = 2)
data
####################### FDTs ANALYSIS
p4 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_READING_TIME , FDT_READING_TIME_Z )
p5 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_COUNTING_TIME , FDT_COUNTING_TIME_Z )
p6 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_CHOOSING_TIME , FDT_CHOOSING_TIME_Z )
p7 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_CHANGING_TIME , FDT_CHANGING_TIME_Z )
p8 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_INHIBITION , FDT_INHIBITION_Z )
p9 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_FLEXIBILITY , FDT_FLEXIBILITY_Z )
######################## P4
coresFDT <- c("#f1948a", "#abebc6")
p4 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", FDT_READING_TIME,
"\nZ-Score: ", FDT_READING_TIME_Z, sep="")) %>%
ggplot(aes(x = AGE, y = FDT_READING_TIME_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresFDT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score FDT Reading Time", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p4
pp4 <- ggplotly(p4, tooltip="text")
pp4
####################### P5
p5 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", FDT_COUNTING_TIME,
"\nZ-Score: ", FDT_COUNTING_TIME_Z, sep="")) %>%
ggplot(aes(x = AGE, y = FDT_COUNTING_TIME_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresFDT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score FDT Counting Time", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p5
pp5 <- ggplotly(p5, tooltip="text")
pp5
####################### P6
p6 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", FDT_CHOOSING_TIME,
"\nZ-Score: ", FDT_CHOOSING_TIME_Z, sep="")) %>%
ggplot(aes(x = AGE, y = FDT_CHOOSING_TIME_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresFDT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score FDT Chossing Time", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p6
pp6 <- ggplotly(p6, tooltip="text")
pp6
####################### P7
p7<- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", FDT_CHANGING_TIME,
"\nZ-Score: ", FDT_CHANGING_TIME_Z, sep="")) %>%
ggplot(aes(x = AGE, y = FDT_CHANGING_TIME_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresFDT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score FDT Changing Time", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p7
pp7 <- ggplotly(p7, tooltip="text")
pp7
####################### P8
p8<- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", FDT_INHIBITION,
"\nZ-Score: ", FDT_INHIBITION_Z, sep="")) %>%
ggplot(aes(x = AGE, y = FDT_INHIBITION_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresFDT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score FDT Inbition", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p8
pp8 <- ggplotly(p8, tooltip="text")
pp8
####################### P9
p9<- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", FDT_FLEXIBILITY,
"\nZ-Score: ", FDT_FLEXIBILITY_Z, sep="")) %>%
ggplot(aes(x = AGE, y = FDT_FLEXIBILITY_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresFDT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score FDT Flexibility", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p9
pp9 <- ggplotly(p9, tooltip="text")
pp9
library(tibble)
library(readxl)
library(dplyr)
library(gtExtras)
library(ggplot2)
library(plotly)
library(viridis)
library(hrbrthemes)
setwd("C:/Users/felip/Desktop/R/3_analise")
data <- readxl::read_xlsx ("BPAs_FDTs_ RAVLTs_EscoreZ.xlsx", sheet = 3)
data
####################### RAVLTs ANALYSIS
p10 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A1 , RAVLT_A1_Z )
p11 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A2 , RAVLT_A2_Z )
p12 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A3 , RAVLT_A3_Z )
p13 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A4 , RAVLT_A4_Z )
p14 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A5 , RAVLT_A5_Z )
p15 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_B1 , RAVLT_B1_Z )
p16 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A6 , RAVLT_A6_Z )
p17 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A7 , RAVLT_A7_Z )
p18 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_TOTALSCORE , RAVLT_TOTALSCORE_Z )
p19 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_REC , RAVLT_REC_Z )
p20 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_RETENTION , RAVLT_RETENTION_Z )
p21 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_PROAT_INTERFERENCE , RAVLT_PROAT_INTERFERENCE_Z )
p22 <- data %>% select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_RETRO_INTERFERENCE , RAVLT_RETRO_INTERFERENCE_Z )
######################## P10
coresRAVLT<- c("#e59866", "#b2babb")
p10 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_A1,
"\nZ-Score: ", RAVLT_A1_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_A1_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT A1", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p10
pp10 <- ggplotly(p10, tooltip="text")
pp10
######################## P11
p11 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_A2,
"\nZ-Score: ", RAVLT_A2_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_A2_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT A2", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p11
pp11 <- ggplotly(p11, tooltip="text")
pp11
######################## P12
p12 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_A3,
"\nZ-Score: ", RAVLT_A3_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_A3_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT A3", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p12
pp12 <- ggplotly(p12, tooltip="text")
pp12
######################## P13
p13 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_A4,
"\nZ-Score: ", RAVLT_A4_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_A4_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT A4", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p13
pp13 <- ggplotly(p13, tooltip="text")
pp13
######################## P14
p14 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_A5,
"\nZ-Score: ", RAVLT_A5_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_A5_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT A4", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p14
pp14 <- ggplotly(p14, tooltip="text")
pp14
######################## P15
p15 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_B1,
"\nZ-Score: ", RAVLT_B1_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_B1_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT B1", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p15
pp15 <- ggplotly(p15, tooltip="text")
pp15
######################## P16
p16 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_A6,
"\nZ-Score: ", RAVLT_A6_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_A6_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT A6", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p16
pp16 <- ggplotly(p16, tooltip="text")
pp16
######################## P17
p17 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_A7,
"\nZ-Score: ", RAVLT_A7_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_A7_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT A7", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p17
pp17 <- ggplotly(p17, tooltip="text")
pp17
######################## P18
p18 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_TOTALSCORE,
"\nZ-Score: ", RAVLT_TOTALSCORE_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_TOTALSCORE_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT Total Score", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p18
pp18 <- ggplotly(p18, tooltip="text")
pp18
######################## P19
p19 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_TOTALSCORE,
"\nZ-Score: ", RAVLT_TOTALSCORE_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_TOTALSCORE_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT Total Score", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p19
pp19 <- ggplotly(p19, tooltip="text")
pp19
######################## P20
p20 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_RETENTION,
"\nZ-Score: ", RAVLT_RETENTION_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_RETENTION_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT Retention", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p20
pp20 <- ggplotly(p20, tooltip="text")
pp20
######################## P21
p21 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_PROAT_INTERFERENCE,
"\nZ-Score: ", RAVLT_PROAT_INTERFERENCE_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_PROAT_INTERFERENCE_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT Proative Interference", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p21
pp21 <- ggplotly(p21, tooltip="text")
pp21
######################## P22
p22 <- data %>%
arrange(ID) %>%
mutate(text = paste("Identification: ", ID , "\nAge: ", AGE ,
"\nPoints: ", RAVLT_RETRO_INTERFERENCE,
"\nZ-Score: ", RAVLT_RETRO_INTERFERENCE_Z, sep="")) %>%
ggplot(aes(x = AGE, y = RAVLT_RETRO_INTERFERENCE_Z, fill= GROUP, stroke = 0,
size = AGE, shape = EDU_LEVEL , text = text)) +
geom_point(alpha = 0.7) +
scale_fill_manual(values = coresRAVLT) +
scale_size(range = c(2, 5)) +
scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) +
geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
labs(y = "Y", title = "Z Score RAVLT Retroative Interference", shape= "", size = "") +
theme_classic() +
theme(
text = element_text(family = "Arial Narrow", size = 11.5),
plot.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 12),
strip.text = element_text(size = 12),
plot.caption = element_text(size = 9, face = "italic"),
axis.title = element_text(size = 10),
legend.position = "",
legend.text = element_text(size = 5)
)
#p22
pp22 <- ggplotly(p22, tooltip="text")
pp22
NA
NA
---
title: "R Notebook"
output: html_notebook
---

```{r warning = F, message = F }
library(tibble)
library(readxl)
library(dplyr)
library(gtExtras)
library(ggplot2)
library(plotly)
library(viridis)
library(hrbrthemes)

setwd("C:/Users/felip/Desktop/R/3_analise")

data <- readxl::read_xlsx ("BPAs_FDTs_ RAVLTs_EscoreZ.xlsx", sheet = 1)

data

#gt_plt_summary(data)



#######################     BPA ANALYSIS


summary(data)

#utils::View(data)


p1 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,EDU_LEVEL , AGE, BPA_CONC_POINTS , BPA_CONC_EscoreZ  )

p2 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,EDU_LEVEL, AGE, BPA_DIVID_POINTS , BPA_DIVID_EscoreZ  )

p3 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,EDU_LEVEL ,AGE, BPA_ALTERN_POINTS , BPA_ALTERN_EscoreZ )



 
 ####################################     P1


coresBPA <- c("#1a2887", "#799de4")



p1 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", BPA_CONC_POINTS,
                      "\nZ-Score: ", BPA_CONC_EscoreZ, sep="")) %>%
  ggplot(aes(x = AGE, y = BPA_CONC_EscoreZ, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresBPA) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score BPA Focused Attention", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p1

pp1 <- ggplotly(p1, tooltip="text") 


pp1


 ##################################       P2

p2 <- data %>%
  arrange(ID) %>%
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", BPA_DIVID_POINTS,
                      "\nZ-Score: ", BPA_DIVID_EscoreZ, sep="")) %>%
  ggplot(aes(x = AGE, y = BPA_DIVID_EscoreZ, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresBPA) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score BPA Focused Attention", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    


#p2

pp2 <- ggplotly(p2, tooltip="text")
 

pp2 



######################################     P3

p3 <- data %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", BPA_ALTERN_POINTS,
                      "\nZ-Score: ", BPA_ALTERN_EscoreZ, sep="")) %>%
  
  ggplot(aes(x = AGE, y = BPA_ALTERN_EscoreZ, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresBPA) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score BPA Focused Attention", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
  

#p3

pp3 <-  ggplotly(p3, tooltip="text")



pp3




``` 


```{r}


library(tibble)
library(readxl)
library(dplyr)
library(gtExtras)
library(ggplot2)
library(plotly)
library(viridis)
library(hrbrthemes)

setwd("C:/Users/felip/Desktop/R/3_analise")

data <- readxl::read_xlsx ("BPAs_FDTs_ RAVLTs_EscoreZ.xlsx", sheet = 2)

data




#######################     FDTs ANALYSIS



p4 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_READING_TIME , FDT_READING_TIME_Z  )

p5 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_COUNTING_TIME , FDT_COUNTING_TIME_Z  )

p6 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_CHOOSING_TIME , FDT_CHOOSING_TIME_Z )

p7 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_CHANGING_TIME , FDT_CHANGING_TIME_Z )

p8 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_INHIBITION , FDT_INHIBITION_Z )

p9 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, FDT_FLEXIBILITY , FDT_FLEXIBILITY_Z )




########################  P4


coresFDT <- c("#f1948a", "#abebc6")



p4 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", FDT_READING_TIME,
                      "\nZ-Score: ", FDT_READING_TIME_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = FDT_READING_TIME_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresFDT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score FDT Reading Time", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p4

pp4 <- ggplotly(p4, tooltip="text") 


pp4

#######################   P5

p5 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", FDT_COUNTING_TIME,
                      "\nZ-Score: ", FDT_COUNTING_TIME_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = FDT_COUNTING_TIME_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresFDT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score FDT Counting Time", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p5

pp5 <- ggplotly(p5, tooltip="text") 


pp5


#######################   P6


p6 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", FDT_CHOOSING_TIME,
                      "\nZ-Score: ", FDT_CHOOSING_TIME_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = FDT_CHOOSING_TIME_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresFDT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score FDT Chossing Time", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p6

pp6 <- ggplotly(p6, tooltip="text") 


pp6


#######################   P7


p7<- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", FDT_CHANGING_TIME,
                      "\nZ-Score: ", FDT_CHANGING_TIME_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = FDT_CHANGING_TIME_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresFDT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score FDT Changing Time", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p7

pp7 <- ggplotly(p7, tooltip="text") 


pp7



#######################   P8


p8<- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", FDT_INHIBITION,
                      "\nZ-Score: ", FDT_INHIBITION_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = FDT_INHIBITION_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresFDT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score FDT Inbition", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p8

pp8 <- ggplotly(p8, tooltip="text") 


pp8


#######################   P9

p9<- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", FDT_FLEXIBILITY,
                      "\nZ-Score: ", FDT_FLEXIBILITY_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = FDT_FLEXIBILITY_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresFDT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score FDT Flexibility", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p9

pp9 <- ggplotly(p9, tooltip="text") 


pp9
``` 


```{r}
library(tibble)
library(readxl)
library(dplyr)
library(gtExtras)
library(ggplot2)
library(plotly)
library(viridis)
library(hrbrthemes)

setwd("C:/Users/felip/Desktop/R/3_analise")

data <- readxl::read_xlsx ("BPAs_FDTs_ RAVLTs_EscoreZ.xlsx", sheet = 3)

data


#######################     RAVLTs ANALYSIS



p10 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A1 , RAVLT_A1_Z  )

p11 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A2 , RAVLT_A2_Z  )

p12 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A3 , RAVLT_A3_Z )

p13 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A4 , RAVLT_A4_Z )

p14 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A5 , RAVLT_A5_Z )

p15 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_B1 , RAVLT_B1_Z )

p16 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A6 , RAVLT_A6_Z )

p17 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_A7 , RAVLT_A7_Z )

p18 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_TOTALSCORE , RAVLT_TOTALSCORE_Z )

p19 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_REC , RAVLT_REC_Z )

p20 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_RETENTION , RAVLT_RETENTION_Z )

p21 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_PROAT_INTERFERENCE , RAVLT_PROAT_INTERFERENCE_Z )

p22 <- data %>%  select(ID,EDU_LEVEL ,GROUP ,AGE, RAVLT_RETRO_INTERFERENCE , RAVLT_RETRO_INTERFERENCE_Z )


########################  P10


coresRAVLT<- c("#e59866", "#b2babb")



p10 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_A1,
                      "\nZ-Score: ", RAVLT_A1_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_A1_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT A1", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p10

pp10 <- ggplotly(p10, tooltip="text") 


pp10

########################  P11


p11 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_A2,
                      "\nZ-Score: ", RAVLT_A2_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_A2_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT A2", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p11

pp11 <- ggplotly(p11, tooltip="text") 


pp11



########################  P12


p12 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_A3,
                      "\nZ-Score: ", RAVLT_A3_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_A3_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT A3", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p12

pp12 <- ggplotly(p12, tooltip="text") 


pp12


########################  P13


p13 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_A4,
                      "\nZ-Score: ", RAVLT_A4_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_A4_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT A4", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p13

pp13 <- ggplotly(p13, tooltip="text") 


pp13


########################  P14


p14 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_A5,
                      "\nZ-Score: ", RAVLT_A5_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_A5_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT A4", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p14

pp14 <- ggplotly(p14, tooltip="text") 


pp14


########################  P15


p15 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_B1,
                      "\nZ-Score: ", RAVLT_B1_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_B1_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT B1", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p15

pp15 <- ggplotly(p15, tooltip="text") 


pp15



########################  P16


p16 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_A6,
                      "\nZ-Score: ", RAVLT_A6_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_A6_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT A6", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p16

pp16 <- ggplotly(p16, tooltip="text") 


pp16



########################  P17

p17 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_A7,
                      "\nZ-Score: ", RAVLT_A7_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_A7_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT A7", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p17

pp17 <- ggplotly(p17, tooltip="text") 


pp17

########################  P18

p18 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_TOTALSCORE,
                      "\nZ-Score: ", RAVLT_TOTALSCORE_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_TOTALSCORE_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT Total Score", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p18

pp18 <- ggplotly(p18, tooltip="text") 


pp18



########################  P19

p19 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_TOTALSCORE,
                      "\nZ-Score: ", RAVLT_TOTALSCORE_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_TOTALSCORE_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT Total Score", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p19

pp19 <- ggplotly(p19, tooltip="text") 


pp19


########################  P20

p20 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_RETENTION,
                      "\nZ-Score: ", RAVLT_RETENTION_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_RETENTION_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT Retention", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p20

pp20 <- ggplotly(p20, tooltip="text") 


pp20


########################  P21

p21 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_PROAT_INTERFERENCE,
                      "\nZ-Score: ", RAVLT_PROAT_INTERFERENCE_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_PROAT_INTERFERENCE_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT Proative Interference", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p21

pp21 <- ggplotly(p21, tooltip="text") 


pp21


########################  P22

p22 <- data %>% 
  arrange(ID) %>% 
  mutate(text = paste("Identification: ", ID , "\nAge: ", AGE , 
                      "\nPoints: ", RAVLT_RETRO_INTERFERENCE,
                      "\nZ-Score: ", RAVLT_RETRO_INTERFERENCE_Z, sep="")) %>%
  ggplot(aes(x = AGE, y = RAVLT_RETRO_INTERFERENCE_Z, fill= GROUP, stroke = 0,
             size = AGE, shape = EDU_LEVEL , text = text)) +
  geom_point(alpha = 0.7) +
  scale_fill_manual(values = coresRAVLT) +
  scale_size(range = c(2, 5)) +
  scale_x_continuous(name = "Age", limits = c(10, 52), breaks = seq(10, 50, by = 10)) +
  scale_y_continuous(name = "Z Score", limits = c(-3, 3), breaks = seq(-3, 3)) +
  geom_hline(yintercept = -3:3, color = "black", alpha = 0.1) + 
  geom_hline(yintercept = 0, color = "red", alpha = 0.3, linetype = "") +
  labs(y = "Y", title = "Z Score RAVLT Retroative Interference", shape= "", size = "") +
  theme_classic() +
  
    theme(
      text = element_text(family = "Arial Narrow", size = 11.5),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 12),
    strip.text = element_text(size = 12),
    plot.caption = element_text(size = 9, face = "italic"),
    axis.title = element_text(size = 10),
    legend.position = "",
    legend.text = element_text(size = 5)
    )
    

#p22

pp22 <- ggplotly(p22, tooltip="text") 


pp22


```